Sierra Nevada Fraction Analysis

Load data

Internal

CN

Read raw

Templates

Merge raw w/ template

14C

Mass

Call:
lm(formula = loss_pct ~ PM + ECO, data = sra.frc.mss.lss.df[-which(sra.frc.mss.lss.df$Probe == 
    "BSpp_comp_2001_18-28"), ])

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1953 -0.9261 -0.4445  0.7965  5.7767 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   4.8136     0.5726   8.406 1.06e-10 ***
PMBS         -0.1405     0.6486  -0.217 0.829539    
PMGR         -2.3959     0.6382  -3.754 0.000507 ***
ECOwf        -1.0117     0.6382  -1.585 0.120098    
ECOrf        -1.7850     0.6394  -2.792 0.007729 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.831 on 44 degrees of freedom
Multiple R-squared:  0.3641,    Adjusted R-squared:  0.3063 
F-statistic: 6.299 on 4 and 44 DF,  p-value: 0.0004255

Interestingly, it appears that AN soils have proportionally LESS minC by mass than do GR or BS soils, significantly so at depth. Why would this be? Possibly because AN soils have higher losses (DOC) during fractionation?

External data

Atmosphere

from C. Rasmussen (’01 C, N; ’09 14C, frc mass, C, N)

calculating Δ14C from fraction modern

Analysis

Misc. functions

Bulk C

2001

2009

2019

Fraction C

C, CN profiles

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C distribution

`summarise()` has grouped output by 'PMeco'. You can override using the `.groups` argument.

Fraction 14C

Depth profiles

Spline ’01, ’09

Spline C

converting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to character

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Spline 14C

C-weighted 14C spline

14C time series

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$`20`

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$emtrends
 PMeco year.trend   SE df lower.CL upper.CL
 ANpp      -6.795 2.07  9   -11.47    -2.12
 ANrf      -1.018 2.07  9    -5.70     3.66
 ANwf      -0.681 2.07  9    -5.36     4.00
 BSpp      -1.842 2.07  9    -6.52     2.84
 BSrf      -2.766 2.07  9    -7.44     1.91
 BSwf      -1.453 2.07  9    -6.13     3.23
 GRpp      -4.189 2.07  9    -8.87     0.49
 GRrf      -0.779 2.07  9    -5.46     3.90
 GRwf      -2.078 2.07  9    -6.76     2.60

Confidence level used: 0.95 

$contrasts
 contrast    estimate   SE df t.ratio p.value
 ANpp - ANrf  -5.7768 2.92  9 -1.975  0.5893 
 ANpp - ANwf  -6.1137 2.92  9 -2.090  0.5287 
 ANpp - BSpp  -4.9532 2.92  9 -1.694  0.7388 
 ANpp - BSrf  -4.0290 2.92  9 -1.378  0.8815 
 ANpp - BSwf  -5.3424 2.92  9 -1.827  0.6689 
 ANpp - GRpp  -2.6063 2.92  9 -0.891  0.9879 
 ANpp - GRrf  -6.0161 2.92  9 -2.057  0.5460 
 ANpp - GRwf  -4.7170 2.92  9 -1.613  0.7792 
 ANrf - ANwf  -0.3370 2.92  9 -0.115  1.0000 
 ANrf - BSpp   0.8235 2.92  9  0.282  1.0000 
 ANrf - BSrf   1.7477 2.92  9  0.598  0.9991 
 ANrf - BSwf   0.4344 2.92  9  0.149  1.0000 
 ANrf - GRpp   3.1704 2.92  9  1.084  0.9630 
 ANrf - GRrf  -0.2394 2.92  9 -0.082  1.0000 
 ANrf - GRwf   1.0598 2.92  9  0.362  1.0000 
 ANwf - BSpp   1.1605 2.92  9  0.397  1.0000 
 ANwf - BSrf   2.0847 2.92  9  0.713  0.9971 
 ANwf - BSwf   0.7714 2.92  9  0.264  1.0000 
 ANwf - GRpp   3.5074 2.92  9  1.199  0.9377 
 ANwf - GRrf   0.0976 2.92  9  0.033  1.0000 
 ANwf - GRwf   1.3967 2.92  9  0.478  0.9998 
 BSpp - BSrf   0.9242 2.92  9  0.316  1.0000 
 BSpp - BSwf  -0.3891 2.92  9 -0.133  1.0000 
 BSpp - GRpp   2.3469 2.92  9  0.802  0.9937 
 BSpp - GRrf  -1.0629 2.92  9 -0.363  1.0000 
 BSpp - GRwf   0.2362 2.92  9  0.081  1.0000 
 BSrf - BSwf  -1.3133 2.92  9 -0.449  0.9999 
 BSrf - GRpp   1.4227 2.92  9  0.486  0.9998 
 BSrf - GRrf  -1.9871 2.92  9 -0.679  0.9979 
 BSrf - GRwf  -0.6880 2.92  9 -0.235  1.0000 
 BSwf - GRpp   2.7360 2.92  9  0.935  0.9838 
 BSwf - GRrf  -0.6737 2.92  9 -0.230  1.0000 
 BSwf - GRwf   0.6254 2.92  9  0.214  1.0000 
 GRpp - GRrf  -3.4098 2.92  9 -1.166  0.9459 
 GRpp - GRwf  -2.1107 2.92  9 -0.722  0.9968 
 GRrf - GRwf   1.2991 2.92  9  0.444  0.9999 

P value adjustment: tukey method for comparing a family of 9 estimates 

Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "fPOM" & dens.01.09.19.sp.df$lyr_bot == 10, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-36.987  -8.459  -3.794  14.059  31.963 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)               165.512     25.131   6.586 0.000101 ***
year_i                     -6.795      2.068  -3.286 0.009443 ** 
pmbasalt                  -43.636     35.541  -1.228 0.250674    
pmgranite                 -23.223     35.541  -0.653 0.529818    
ecocool                  -168.432     35.541  -4.739 0.001060 ** 
ecocold                  -104.629     35.541  -2.944 0.016382 *  
year_i:pmbasalt             4.953      2.925   1.694 0.124591    
year_i:pmgranite            2.606      2.925   0.891 0.396052    
year_i:ecocool              6.114      2.925   2.090 0.066144 .  
year_i:ecocold              5.777      2.925   1.975 0.079678 .  
pmbasalt:ecocool           93.779     50.262   1.866 0.094932 .  
pmgranite:ecocool         103.988     50.262   2.069 0.068483 .  
pmbasalt:ecocold           62.329     50.262   1.240 0.246303    
pmgranite:ecocold          17.972     50.262   0.358 0.728903    
year_i:pmbasalt:ecocool    -5.725      4.136  -1.384 0.199702    
year_i:pmgranite:ecocool   -4.003      4.136  -0.968 0.358422    
year_i:pmbasalt:ecocold    -6.701      4.136  -1.620 0.139664    
year_i:pmgranite:ecocold   -2.367      4.136  -0.572 0.581154    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 26.38 on 9 degrees of freedom
Multiple R-squared:  0.8821,    Adjusted R-squared:  0.6595 
F-statistic: 3.962 on 17 and 9 DF,  p-value: 0.02039


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "fPOM" & dens.01.09.19.sp.df$lyr_bot == 20, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-26.206  -9.681  -4.089   6.602  47.172 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)  
(Intercept)                78.039     28.446   2.743   0.0227 *
year_i                     -4.217      2.341  -1.801   0.1052  
pmbasalt                    2.083     40.229   0.052   0.9598  
pmgranite                 -34.956     40.229  -0.869   0.4075  
ecocool                  -127.141     40.229  -3.160   0.0115 *
ecocold                   -68.688     40.229  -1.707   0.1219  
year_i:pmbasalt             3.623      3.311   1.094   0.3022  
year_i:pmgranite            8.217      3.311   2.482   0.0349 *
year_i:ecocool              4.120      3.311   1.245   0.2447  
year_i:ecocold              3.572      3.311   1.079   0.3086  
pmbasalt:ecocool           71.432     56.892   1.256   0.2409  
pmgranite:ecocool         113.279     56.892   1.991   0.0777 .
pmbasalt:ecocold           47.147     56.892   0.829   0.4287  
pmgranite:ecocold          37.800     56.892   0.664   0.5231  
year_i:pmbasalt:ecocool    -6.787      4.682  -1.450   0.1811  
year_i:pmgranite:ecocool  -10.673      4.682  -2.280   0.0486 *
year_i:pmbasalt:ecocold    -5.429      4.682  -1.160   0.2761  
year_i:pmgranite:ecocold   -8.132      4.682  -1.737   0.1164  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 29.86 on 9 degrees of freedom
Multiple R-squared:  0.8625,    Adjusted R-squared:  0.6027 
F-statistic:  3.32 on 17 and 9 DF,  p-value: 0.03583


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "fPOM" & dens.01.09.19.sp.df$lyr_bot == 30, ])

Residuals:
   Min     1Q Median     3Q    Max 
-32.57 -11.84  -3.65   7.70  58.63 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)
(Intercept)               -7.7862    34.5553  -0.225    0.827
year_i                    -0.5033     2.8436  -0.177    0.863
pmbasalt                  57.2270    48.8686   1.171    0.272
pmgranite                 19.0016    48.8686   0.389    0.706
ecocool                  -67.2614    48.8686  -1.376    0.202
ecocold                  -27.5273    48.8686  -0.563    0.587
year_i:pmbasalt            0.5886     4.0215   0.146    0.887
year_i:pmgranite           3.3000     4.0215   0.821    0.433
year_i:ecocool             0.9728     4.0215   0.242    0.814
year_i:ecocold             1.3306     4.0215   0.331    0.748
pmbasalt:ecocool          45.7950    69.1106   0.663    0.524
pmgranite:ecocool         65.6280    69.1106   0.950    0.367
pmbasalt:ecocold          -1.7179    69.1106  -0.025    0.981
pmgranite:ecocold          3.6995    69.1106   0.054    0.958
year_i:pmbasalt:ecocool   -7.3011     5.6873  -1.284    0.231
year_i:pmgranite:ecocool  -5.8728     5.6873  -1.033    0.329
year_i:pmbasalt:ecocold   -2.4287     5.6873  -0.427    0.679
year_i:pmgranite:ecocold  -3.7023     5.6873  -0.651    0.531

Residual standard error: 36.27 on 9 degrees of freedom
Multiple R-squared:  0.776, Adjusted R-squared:  0.353 
F-statistic: 1.835 on 17 and 9 DF,  p-value: 0.178


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "minC" & dens.01.09.19.sp.df$lyr_bot == 10, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-32.445 -14.690  -0.982   8.313  49.666 

Coefficients:
                            Estimate  Std. Error t value Pr(>|t|)  
(Intercept)                53.235715   31.836582   1.672   0.1288  
year_i                     -1.368463    2.619903  -0.522   0.6141  
pmbasalt                  -11.392863   45.023726  -0.253   0.8059  
pmgranite                  13.808110   45.023726   0.307   0.7661  
ecocool                  -129.550490   45.023726  -2.877   0.0183 *
ecocold                   -53.252323   45.023726  -1.183   0.2672  
year_i:pmbasalt             1.011690    3.705102   0.273   0.7910  
year_i:pmgranite           -0.008576    3.705102  -0.002   0.9982  
year_i:ecocool              3.341545    3.705102   0.902   0.3906  
year_i:ecocold             -0.800799    3.705102  -0.216   0.8337  
pmbasalt:ecocool          100.351702   63.673164   1.576   0.1495  
pmgranite:ecocool         101.017634   63.673164   1.587   0.1471  
pmbasalt:ecocold           -2.086764   63.673164  -0.033   0.9746  
pmgranite:ecocold         -45.174314   63.673164  -0.709   0.4960  
year_i:pmbasalt:ecocool    -2.576587    5.239806  -0.492   0.6347  
year_i:pmgranite:ecocool   -3.747488    5.239806  -0.715   0.4926  
year_i:pmbasalt:ecocold     2.650378    5.239806   0.506   0.6251  
year_i:pmgranite:ecocold    4.370745    5.239806   0.834   0.4258  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 33.41 on 9 degrees of freedom
Multiple R-squared:  0.7665,    Adjusted R-squared:  0.3254 
F-statistic: 1.738 on 17 and 9 DF,  p-value: 0.2008


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "minC" & dens.01.09.19.sp.df$lyr_bot == 20, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-25.918  -9.446  -2.474   9.149  31.546 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -9.5997    21.9478  -0.437   0.6721  
year_i                     0.7565     1.8061   0.419   0.6851  
pmbasalt                  -8.2247    31.0389  -0.265   0.7970  
pmgranite                -11.4347    31.0389  -0.368   0.7211  
ecocool                  -94.7296    31.0389  -3.052   0.0137 *
ecocold                  -15.3565    31.0389  -0.495   0.6326  
year_i:pmbasalt            0.7698     2.5543   0.301   0.7700  
year_i:pmgranite           3.0074     2.5543   1.177   0.2692  
year_i:ecocool             1.2303     2.5543   0.482   0.6415  
year_i:ecocold            -2.6857     2.5543  -1.051   0.3205  
pmbasalt:ecocool         127.3757    43.8956   2.902   0.0175 *
pmgranite:ecocool        124.8421    43.8956   2.844   0.0193 *
pmbasalt:ecocold          -0.9729    43.8956  -0.022   0.9828  
pmgranite:ecocold        -31.2191    43.8956  -0.711   0.4950  
year_i:pmbasalt:ecocool   -4.6467     3.6123  -1.286   0.2304  
year_i:pmgranite:ecocool  -5.8824     3.6123  -1.628   0.1379  
year_i:pmbasalt:ecocold    2.9547     3.6123   0.818   0.4345  
year_i:pmgranite:ecocold   2.1864     3.6123   0.605   0.5599  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 23.04 on 9 degrees of freedom
Multiple R-squared:  0.8619,    Adjusted R-squared:  0.6011 
F-statistic: 3.305 on 17 and 9 DF,  p-value: 0.03635


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "minC" & dens.01.09.19.sp.df$lyr_bot == 30, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-21.580  -9.094   3.950   6.546  38.845 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -54.2864    20.6763  -2.626   0.0276 *
year_i                     1.7628     1.7015   1.036   0.3272  
pmbasalt                  -4.1368    29.2407  -0.141   0.8906  
pmgranite                -39.1905    29.2407  -1.340   0.2130  
ecocool                  -58.1383    29.2407  -1.988   0.0780 .
ecocold                   15.4194    29.2407   0.527   0.6107  
year_i:pmbasalt           -0.1398     2.4063  -0.058   0.9550  
year_i:pmgranite           3.6500     2.4063   1.517   0.1636  
year_i:ecocool            -1.0213     2.4063  -0.424   0.6812  
year_i:ecocold            -5.3002     2.4063  -2.203   0.0551 .
pmbasalt:ecocool         132.3396    41.3525   3.200   0.0108 *
pmgranite:ecocool        133.4644    41.3525   3.227   0.0104 *
pmbasalt:ecocold         -34.7870    41.3525  -0.841   0.4220  
pmgranite:ecocold         -6.5936    41.3525  -0.159   0.8768  
year_i:pmbasalt:ecocool   -4.8607     3.4030  -1.428   0.1870  
year_i:pmgranite:ecocool  -4.8928     3.4030  -1.438   0.1843  
year_i:pmbasalt:ecocold    6.4371     3.4030   1.892   0.0911 .
year_i:pmgranite:ecocold   3.3074     3.4030   0.972   0.3565  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 21.7 on 9 degrees of freedom
Multiple R-squared:  0.8748,    Adjusted R-squared:  0.6383 
F-statistic: 3.699 on 17 and 9 DF,  p-value: 0.0255

NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions

Char pool calc

`summarise()` has grouped output by 'PMeco', 'pm', 'eco', 'lyr_bot', 'soc_POM'. You can override using the `.groups` argument.

Fraction vs respired 14C

NOTE: Results may be misleading due to involvement in interactions

Thermal fractions

C release (thermograms)

Thermal fraction 14C

Attaching package: ‘gridExtra’

The following object is masked from ‘package:dplyr’:

    combine

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Attaching package: ‘mclust’

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package ‘sjstats’ was built under R version 3.6.2Registered S3 method overwritten by 'parameters':
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Attaching package: ‘sjstats’

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Loading required package: lattice
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Attaching package: ‘Hmisc’

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Currently analysing dataframe 
Between 0 and 140 degrees C the proportion of sample C is 0.001488078 
Between 140 and 245 degrees C the proportion of sample C is 0.03870194 
Between 245 and 340 degrees C the proportion of sample C is 0.3571529 
Between 340 and 390 degrees C the proportion of sample C is 0.2355014 
Between 390 and 495 degrees C the proportion of sample C is 0.2936209 
Between 495 and 791 degrees C the proportion of sample C is 0.07359725 
Currently analysing dataframe 
Between 0 and 141.5 degrees C the proportion of sample C is 0.002647777 
Between 141.5 and 246.5 degrees C the proportion of sample C is 0.07616599 
Between 246.5 and 290 degrees C the proportion of sample C is 0.1643875 
Between 290 and 364 degrees C the proportion of sample C is 0.3792163 
Between 364 and 484 degrees C the proportion of sample C is 0.3074933 
Between 484 and 788 degrees C the proportion of sample C is 0.07006177 
Currently analysing dataframe 
Between 0 and 143 degrees C the proportion of sample C is 0.005167379 
Between 143 and 248 degrees C the proportion of sample C is 0.07075111 
Between 248 and 293 degrees C the proportion of sample C is 0.163502 
Between 293 and 367 degrees C the proportion of sample C is 0.3797734 
Between 367 and 487 degrees C the proportion of sample C is 0.3038745 
Between 487 and 792 degrees C the proportion of sample C is 0.07718191